Summary Machine Learning Interatomic Potential (MLIP) overcomes the challenges of high
computational costs in density-functional theory and the relatively low accuracy in classical …

Integration of chalcogenide phase-change materials (PCMs) with planar multilayer
structures, metasurfaces, waveguides and photonic integrated circuits has sparked …

Photoelectrochemical (PEC) water splitting is a promising technology for green hydrogen
production. However, severe charge recombination in the photoelectrode materials is one of …

In the past decade, there has been tremendous progress in integrating chalcogenide phase-
change materials (PCMs) on the silicon photonic platform for non-volatile memory to …

Chalcogenide phase-change materials (PCMs) are showing versatile possibilities in cutting-
edge applications, including non-volatile memory, neuromorphic computing, and nano …

Understanding the crystallization dynamics of chalcogenide phase-change materials
(PCMs) is crucial for optimizing their performance in data storage and neuromorphic …

The development of the next generation of optical phase change technologies for integrated
photonic and free-space platforms relies on the availability of materials that can be switched …

The disorder‐to‐order (crystallization) process in phase‐change materials determines the
speed and storage polymorphism of phase‐change memory devices. Only by clarifying the …

Chalcogenide phase-change materials (PCMs) are among the most mature candidates for
next-generation memory technology. Recently, CrGeTe3 (CrGT) emerged as a promising …

Computer simulations have long been key to understanding and designing phase-change
materials (PCMs) for memory technologies. Machine learning is now increasingly being …